OPEN LECTURE: Personalisation in MarTech
- Megi Gogua
- May 22
- 3 min read
ntroduction
Hello everyone!
I’m really happy to welcome you to my Online Open MarTech lecture.
My name is Megi, I have taught and studied MarTech for many years already, and with this series of Online Open Lectures, I would like to share with you what I know so far.
This series of lectures will contain all the topics related to MarTech, from broad approaches to certain examples and cases.
These lectures are for those who consider MarTech to be the topic, field, or direction of their career development. Also, these videos are for those who already have marketing experience but want to see a broader horizon and more examples of the application of technologies in marketing. Those who generally wish to learn something new can also benefit from these recordings of Online Open Lectures.
I really hope that this initiative of mine will be useful for you and helpful because I would love to share with you everything I know and invite you to this wonderful journey of finding something new, something interesting. If you ask me, this field of MarTech is incredibly fascinating and useful, and I’m happy to be able to guide you through it.
Personalisation is rooted in MarTech - today, we will discuss this fundamental aspect.
Introduction of me as a lecturer
Before we get to the lecture, I would like to introduce myself with this slide.
Full information about my Education and Experience is available here:
What is the definition of Personalisation?
Personalisation is a customer-oriented marketing strategy that aims to deliver the right content to the right person at the right time, to maximize immediate and future business opportunities [1]
Personalisation is the matching of advertising content and vendors' services with customers based on their preferences and individual needs [2]
Personalisation is using data to target and retarget leads with a brand message that speaks directly to their specific interests, demographics, and buying behavior [3]
Level of granularity in Personalization [4]
· Mass - all consumers receive the same offering and/or marketing mix, personalized to their average taste
· Segment-level - groups of consumers with homogeneous preferences are identified and the marketing mix is personalized in the same way for all consumers in one segment
· Individual-level - each consumer receives offerings and/or elements of the marketing mix customized to his or her individual tastes and behaviors
Let's consider different types of Recommender systems
Methods in Personalisation [4]
· Pull - personalized service when a customer explicitly requests it.
· Passive - displays personalized information about products or services in response to related customer activities, but the consumer has to act on that information
· Push - takes passive personalization one step further by sending a personalized product or service directly to customers without their explicit request
Recommendation types [4]
Content Filtering – recommendations based on the similarity between a customer’s past preferences for products and services
Collaborative Filtering - predicts a customer’s preferences using those of similar customer
Hybrid recommendations - hybrid recommendation systems combine features of both content filtering and collaborative filtering
We will discuss the applications in one of the future posts!
I hope it was useful! Do not hesitate to share this article with anyone you consider to be interested in this topic.
If you wish to learn more, you can follow the links:
Explore the MarTech field
Learn about MarTech: Attend my Open Online MarTech Lectures
Talk about MarTech: Participate in the “MarTalks” – my open speaking/discussion club
Practice for your MarTech communications: Complete my Free English Exercises for Marketers
Excel in your MarTech career
Learn about your MarTech career possibilities:
Explore the specificities of the developing MarTech field by going through my guide – The MarTech Guide
Ask me directly – register for a consultation with me to learn about the MarTech career trajectories, required skills and examples of the tasks
Improve your English for marketing purposes
Register for the Individual consultation or lessons based on your request (ex. presentation rehearsal, preparation for negotiations, MarTech terms and vocabulary enrichment)
Register to develop your individual learning programme for a chosen career path in MarTech
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Sources:
[1] Definition of data from the Cambridge Advanced Learner's Dictionary & Thesaurus
[2] Brinker Scott, "Hacking Marketing: Agile Practices to Make Marketing Smarter, Faster, and More Innovative", Wiley; 1st edition (March 4, 2016)
[3] Amazon Web Services (AWS), What is Structured Data? https://aws.amazon.com/what-is/structured-data/
[4] IBM. Structured versus unstructured data: What's the difference? https://www.ibm.com/think/topics/structured-vs-unstructured-data
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